Most traditional speech recognition (detection) systems ignore words that are not in the recognizer's dictionary vocabulary (out-of-vocabulary or OOV words). At most, some traditional speech recognition systems attempt to minimize the effect of such OOV words by selecting a vocabulary that is closely matched to the domain and that is as large as possible. In the area of spoken document retrieval there are almost always OOV words with respect to the vocabulary that is used. Potentially, the OOV words may be also the most interesting words for indexing purposes.
Traditional methods for detecting OOV words have focused primarily on phoneme or syllable-based recognition systems, which place no restrictions on the words to be recognized. Because of the poor error rate of traditional phoneme based recognition, the best hypothesis is not good for indexing, since many OOV words would be lost. Instead, phoneme lattices must be stored and searched anew each time a query is made. The search thus scales approximately linearly in the size of the data which results in a slow search compared to word-based retrieval techniques. In contrast, traditional word-based indexing involves a simple look-up of the query word in a hash table to retrieve documents in which the query word occurs. This search is approximately constant in the size of the data.